Abstract
Dense ground displacement measurements are crucial for geological studies but are impractical to collect directly. Traditionally, displacement fields are estimated using patch matching on optical satellite images from different acquisition times. While deep learning-based optical flow models are promising, their adoption in ground deformation analysis is hindered by challenges such as the absence of real ground truth, the need for sub-pixel precision, and temporal variations due to geological or anthropogenic changes. In particular, we identify that deep learning models relying on explicit correlation layers struggle at estimating small displacements in real-world conditions. Instead, we propose a model that employs iterative refinements with explicit warping layers and a correlation-independent backbone, enabling sub-pixel precision. Additionally, a non-convex variant of Total Variation regularization preserves fault-line sharpness while maintaining smoothness elsewhere. Our model significantly outperforms widely used geophysics methods on semi-synthetic benchmarks and generalizes well to challenging real-world scenarios captured by both medium- and high-resolution sensors. Project page: this https URL.
Abstract (translated)
密集地面位移测量对于地质研究至关重要，但直接收集这些数据是不切实际的。传统上，通过不同时间获取的光学卫星图像进行块匹配来估计位移场。虽然基于深度学习的光流模型前景广阔，但在地表变形分析中的应用却因缺乏真实的数据基准、需要亚像素精度以及由于地质或人为变化引起的时序波动等挑战而受阻。特别地，我们发现依赖于显式相关层的深度学习模型在实际条件下估计微小位移方面存在困难。 为此，我们提出了一种采用迭代细化和显式的变形层，并且与相关性无关的基础架构的方法，这使得亚像素精度成为可能。此外，非凸Total Variation正则化方法可以保持断层面的锐度，同时确保其他区域的平滑性。我们的模型在半合成基准测试中显著优于广泛使用的地球物理方法，并且能够很好地推广到由中高分辨率传感器捕捉的各种实际挑战场景中。 项目页面: [此链接](https://this https URL)
URL
https://arxiv.org/abs/2504.13452
